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A Poisson Factor Mixture Model for the Analysis of Linguistic Competence in Italian University Students' Writing

Silvia Dallari, Laura Anderlucci, Nicola Grandi, Angela Montanari

Abstract

Public debate on the alleged decline of language skills among younger generations often focuses on university students, the most highly educated segment of the population. Rather than addressing the ill posed question of linguistic decline, this paper examines how formal written Italian is currently used by university students and whether systematic patterns of competence and heterogeneity can be identified. The analysis is based on data from the UniversITA project, which collected formal texts written by a large and nationally representative sample of Italian university students. Texts were annotated for linguistically motivated features covering orthography, lexicon, syntax, morphosyntax, coherence, register, and sentence structure, yielding low frequency multivariate count data. To analyse these data, we propose a novel model-based clustering approach based on a Poisson factor mixture model that accounts for dependence among linguistic features and unobserved population heterogeneity. The results identify two correlated dimensions of writing competence, interpretable as communicative competence and linguistic grammatical competence. When educational and socio demographic information is incorporated, distinct student profiles emerge that are associated with field of study and educational background. These findings provide quantitative evidence on contemporary writing and offer insights relevant for language education and higher education policy.

A Poisson Factor Mixture Model for the Analysis of Linguistic Competence in Italian University Students' Writing

Abstract

Public debate on the alleged decline of language skills among younger generations often focuses on university students, the most highly educated segment of the population. Rather than addressing the ill posed question of linguistic decline, this paper examines how formal written Italian is currently used by university students and whether systematic patterns of competence and heterogeneity can be identified. The analysis is based on data from the UniversITA project, which collected formal texts written by a large and nationally representative sample of Italian university students. Texts were annotated for linguistically motivated features covering orthography, lexicon, syntax, morphosyntax, coherence, register, and sentence structure, yielding low frequency multivariate count data. To analyse these data, we propose a novel model-based clustering approach based on a Poisson factor mixture model that accounts for dependence among linguistic features and unobserved population heterogeneity. The results identify two correlated dimensions of writing competence, interpretable as communicative competence and linguistic grammatical competence. When educational and socio demographic information is incorporated, distinct student profiles emerge that are associated with field of study and educational background. These findings provide quantitative evidence on contemporary writing and offer insights relevant for language education and higher education policy.
Paper Structure (15 sections, 42 equations, 7 figures, 8 tables)

This paper contains 15 sections, 42 equations, 7 figures, 8 tables.

Table of Contents

  1. Introduction
  2. The data
  3. Poisson factor mixture model for multivariate count data
  4. Identifiability conditions
  5. Model Estimation
  6. Model selection
  7. Empirical Results
  8. Simulation study
  9. Real Data Analysis
  10. Extension with covariates
  11. Discussion
  12. Data
  13. Generalized EM algorithm
  14. Simulation Study
  15. Acknowledgments

Figures (7)

  • Figure 1: Scatterplot of the factor scores distinguished by group for one of the 100 replicates of the experiment with $q=2$, $k=3$, $n=300$ and $p=50$.
  • Figure 2: Ability of recovering the true clustering structure (with known $q$ and $k$) for the different simulation designs considered.
  • Figure 3: Contours of the 2D kernel densities of the three groups obtained for the Univers-ITA dataset with covariates. On both dimensions lower values represent better results.
  • Figure 4: Percentage of students belonging to one of the three groups for each category of the covariates considered for the Univers-ITA dataset.
  • Figure A1: Boxplots representing the distribution of the number of annotations for each feature considered in the analysis.
  • ...and 2 more figures